In vision-aided navigation, a map may be used to provide absolute position updates. This process works by comparing the image of a scene viewed by a camera to a database of images at known locations. The relative offset between these two images reflects the error in the current navigation state estimate. Maintaining a database of images for navigation requires a great deal of storage, such as tens or hundreds of gigabytes for realistic missions.
One conventional approach to image matching uses edge features or boundaries of objects shown in the images. The edges can be extracted from an image using one of many known edge detection routines, such as the Canny or Sobel methods. After extraction, edges from the camera image and database image can be compared for use in vision-aided navigation as described above. Edges from an image are advantageous to use because they are robust to different sensor bands. For example, edges from an infrared image often match edges from an optical-band image very well.
Most image matching methods store whole imagery for navigation. Although compression, such as JPEG, is often used, this still requires a very large database to cover areas of interest for a mission, requiring the use of an expensive, power-hungry, and unreliable hard drive to store the images.